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Evolving Hidden Markov Models for protein secondary structure prediction

Publikation: Bidrag til bog/antologi/rapportKonferencebidrag i proceedingsForskningfagfællebedømt

New results are presented for the prediction of secondary structure information for protein sequences using Hidden Markov Models (HMMs) evolved using a Genetic Algorithm (GA). We achieved a Q 3 measure of 75% using one of the most stringent data set ever used for protein secondary structure prediction. Our results beat the best hand-designed HMM currently available and are comparable to the best known techniques for this problem. A hybrid GA incorporating the Baum-Welch algorithm was used. The topology of the HMM was restricted to biologically meaningful building blocks. Mutation and crossover operators were designed to explore this space of topologies.

OriginalsprogEngelsk
Titel2005 IEEE Congress on Evolutionary Computation, IEEE CEC 2005. Proceedings
Antal sider8
Vol/bind1
Publikationsdato31 okt. 2005
Sider33-40
ISBN (Trykt)0780393635, 9780780393639
StatusUdgivet - 31 okt. 2005
Begivenhed2005 IEEE Congress on Evolutionary Computation, IEEE CEC 2005 - Edinburgh, Scotland, Storbritannien
Varighed: 2 sep. 20055 sep. 2005

Konference

Konference2005 IEEE Congress on Evolutionary Computation, IEEE CEC 2005
LandStorbritannien
ByEdinburgh, Scotland
Periode02/09/200505/09/2005

ID: 199873169